Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.7 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Warnings

gross_revenue is highly correlated with qtde_itemsHigh correlation
qtde_items is highly correlated with gross_revenueHigh correlation
avg_ticket is highly correlated with qtde_returns and 1 other fieldsHigh correlation
qtde_returns is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422362) Skewed
qtde_returns is highly skewed (γ1 = 51.79774426) Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661) Skewed
df_index has unique values Unique
customer_id has unique values Unique
avg_ticket has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2021-05-19 00:39:41.141319
Analysis finished2021-05-19 00:40:13.619009
Duration32.48 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.292354
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:13.775165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.944589
Coefficient of variation (CV)0.6710178739
Kurtosis-1.010787014
Mean2317.292354
Median Absolute Deviation (MAD)1271
Skewness0.342284058
Sum6880041
Variance2417852.674
MonotonicityStrictly increasing
2021-05-18T21:40:14.010024image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
26541
 
< 0.1%
26441
 
< 0.1%
5971
 
< 0.1%
26461
 
< 0.1%
5991
 
< 0.1%
26481
 
< 0.1%
6011
 
< 0.1%
6031
 
< 0.1%
51441
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.77299
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:14.308569image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.990292
Coefficient of variation (CV)0.1125673398
Kurtosis-1.206094692
Mean15270.77299
Median Absolute Deviation (MAD)1488
Skewness0.03160785866
Sum45338925
Variance2954927.624
MonotonicityNot monotonic
2021-05-18T21:40:14.870314image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
181641
 
< 0.1%
129331
 
< 0.1%
129351
 
< 0.1%
149841
 
< 0.1%
170331
 
< 0.1%
137041
 
< 0.1%
129391
 
< 0.1%
170371
 
< 0.1%
141251
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2963
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.321711
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:15.023954image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.62331
Coefficient of variation (CV)3.848448607
Kurtosis353.944724
Mean2749.321711
Median Absolute Deviation (MAD)672.16
Skewness16.77755612
Sum8162736.16
Variance111949589.6
MonotonicityNot monotonic
2021-05-18T21:40:15.162312image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
379.652
 
0.1%
533.332
 
0.1%
745.062
 
0.1%
734.942
 
0.1%
731.92
 
0.1%
3312
 
0.1%
719.781
 
< 0.1%
13375.871
 
< 0.1%
447.641
 
< 0.1%
567.361
 
< 0.1%
Other values (2953)2953
99.5%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.28763894
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:15.311109image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75677911
Coefficient of variation (CV)1.209513686
Kurtosis2.777962659
Mean64.28763894
Median Absolute Deviation (MAD)26
Skewness1.798379538
Sum190870
Variance6046.116697
MonotonicityNot monotonic
2021-05-18T21:40:15.479992image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%

qtde_invoices
Real number (ℝ≥0)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.723139104
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:15.670315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.85653132
Coefficient of variation (CV)1.547495379
Kurtosis190.8344494
Mean5.723139104
Median Absolute Deviation (MAD)2
Skewness10.76680458
Sum16992
Variance78.43814702
MonotonicityNot monotonic
2021-05-18T21:40:15.878475image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.852476
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:16.110147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578045
Coefficient of variation (CV)3.659489067
Kurtosis465.998084
Mean1608.852476
Median Absolute Deviation (MAD)422
Skewness17.85859125
Sum4776683
Variance34663575.24
MonotonicityNot monotonic
2021-05-18T21:40:16.335396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2888
 
0.3%
2728
 
0.3%
848
 
0.3%
2468
 
0.3%
2608
 
0.3%
4937
 
0.2%
1347
 
0.2%
Other values (1661)2886
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%

qtde_products
Real number (ℝ≥0)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7241495
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:16.575460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8964081
Coefficient of variation (CV)2.199211884
Kurtosis354.8611303
Mean122.7241495
Median Absolute Deviation (MAD)44
Skewness15.70763473
Sum364368
Variance72844.07112
MonotonicityNot monotonic
2021-05-18T21:40:16.798174image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2629
88.5%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.89776151
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:16.994728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.916661099
Q113.11933333
median17.95658654
Q324.98828571
95-th percentile90.497
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86895238

Descriptive statistics

Standard deviation1036.934407
Coefficient of variation (CV)19.98033011
Kurtosis2890.707126
Mean51.89776151
Median Absolute Deviation (MAD)5.984842033
Skewness53.44422362
Sum154084.4539
Variance1075232.964
MonotonicityNot monotonic
2021-05-18T21:40:17.178597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.492758621
 
< 0.1%
15.413636361
 
< 0.1%
18.150615381
 
< 0.1%
17.943444441
 
< 0.1%
43.21921
 
< 0.1%
33.535714291
 
< 0.1%
9.4182926831
 
< 0.1%
19.557670451
 
< 0.1%
132.07389831
 
< 0.1%
16.807222221
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.34851138
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:17.380161image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92307692
median48.28571429
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.41025641

Descriptive statistics

Standard deviation63.54492876
Coefficient of variation (CV)0.9435238799
Kurtosis4.887109087
Mean67.34851138
Median Absolute Deviation (MAD)26.28571429
Skewness2.062770925
Sum199957.7303
Variance4037.957972
MonotonicityNot monotonic
2021-05-18T21:40:17.552339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
516
 
0.5%
Other values (1248)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
ValueCountFrequency (%)
3661
< 0.1%
3651
< 0.1%
3631
< 0.1%
3621
< 0.1%
3572
0.1%

frequency
Real number (ℝ≥0)

Distinct1350
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06327807795
Minimum0.005449591281
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:17.709062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.009433962264
Q10.01777777778
median0.02941176471
Q30.05540166205
95-th percentile0.2222222222
Maximum3
Range2.994550409
Interquartile range (IQR)0.03762388427

Descriptive statistics

Standard deviation0.1344820641
Coefficient of variation (CV)2.125255198
Kurtosis121.5575473
Mean0.06327807795
Median Absolute Deviation (MAD)0.01433823529
Skewness8.773259386
Sum187.8726134
Variance0.01808542557
MonotonicityNot monotonic
2021-05-18T21:40:17.843315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.166666666721
 
0.7%
0.333333333321
 
0.7%
0.0277777777820
 
0.7%
0.0909090909119
 
0.6%
0.062517
 
0.6%
0.133333333316
 
0.5%
0.416
 
0.5%
0.2515
 
0.5%
0.0238095238115
 
0.5%
0.0357142857115
 
0.5%
Other values (1340)2794
94.1%
ValueCountFrequency (%)
0.0054495912811
< 0.1%
0.0054644808741
< 0.1%
0.0054945054951
< 0.1%
0.0055096418731
< 0.1%
0.0055865921792
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
21
 
< 0.1%
1.5714285711
 
< 0.1%
1.53
 
0.1%
114
0.5%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.1569552
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:18.013481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.496135
Coefficient of variation (CV)24.33349783
Kurtosis2765.52864
Mean62.1569552
Median Absolute Deviation (MAD)1
Skewness51.79774426
Sum184544
Variance2287644.557
MonotonicityNot monotonic
2021-05-18T21:40:18.212914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (204)706
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.8137641
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:18.371252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.3333333
Q3281.6923077
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.4423077

Descriptive statistics

Standard deviation791.5551894
Coefficient of variation (CV)3.168581172
Kurtosis2255.538236
Mean249.8137641
Median Absolute Deviation (MAD)83.08333333
Skewness44.67271661
Sum741697.0657
Variance626559.6179
MonotonicityNot monotonic
2021-05-18T21:40:18.525430image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
739
 
0.3%
869
 
0.3%
1368
 
0.3%
758
 
0.3%
888
 
0.3%
608
 
0.3%
1637
 
0.2%
Other values (1969)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

Distinct1005
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.1547082
Minimum1
Maximum299.7058824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-05-18T21:40:18.681227image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.345454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.7058824
Range298.7058824
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.51232207
Coefficient of variation (CV)0.8807302672
Kurtosis27.70329723
Mean22.1547082
Median Absolute Deviation (MAD)8.2
Skewness3.499455899
Sum65777.32865
Variance380.7307127
MonotonicityNot monotonic
2021-05-18T21:40:18.824864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1353
 
1.8%
1439
 
1.3%
1138
 
1.3%
2033
 
1.1%
933
 
1.1%
132
 
1.1%
1731
 
1.0%
1030
 
1.0%
1830
 
1.0%
1629
 
1.0%
Other values (995)2621
88.3%
ValueCountFrequency (%)
132
1.1%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
ValueCountFrequency (%)
299.70588241
< 0.1%
2591
< 0.1%
203.51
< 0.1%
1481
< 0.1%
1451
< 0.1%

Interactions

2021-05-18T21:39:44.961079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:45.137761image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:45.290767image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:45.443216image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:45.595119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:45.759921image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:45.923354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:46.082766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:46.242742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:46.404920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:46.549613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:46.706625image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:46.866149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:47.233904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:47.379039image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:47.523749image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:47.657962image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:47.806386image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:47.959472image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:48.110791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:48.298196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:48.462280image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:48.635414image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:48.849283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:49.044933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:49.209892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:49.378441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:49.565522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:49.736417image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:49.922385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:50.103878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:50.252290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:50.438142image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:50.625166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:50.812057image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:50.997501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:51.167281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:51.345940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:51.503101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:51.663257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:51.808594image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:51.976698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:52.143444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:52.267799image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:52.419957image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:52.560000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:52.712975image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:52.877623image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:53.051421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:53.208463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:53.411801image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:53.639492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:53.866241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:54.122464image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:54.478183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:54.629756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:54.795911image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:54.930839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:55.094559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:55.289142image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:55.474183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:55.689886image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:55.886770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:56.097953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:56.299226image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:56.494445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:56.702458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:56.850135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:56.983110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:57.151893image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:57.338103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:57.516059image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:57.690241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:57.864782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:58.064448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:58.253925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:58.474275image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:58.660539image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:58.868355image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:59.031974image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:59.223090image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:59.407184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:59.561402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:59.730061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:39:59.903737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:00.084638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:00.265728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:00.470513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:00.675521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:00.833141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:01.011511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:01.209476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:01.416418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:01.601559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:01.763107image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:01.877207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:01.989338image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:02.142188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:02.291138image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:02.440805image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:02.587787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:02.747366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:02.900527image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:03.091470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:03.484598image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:03.673016image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:03.872874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:04.104887image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:04.297775image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:04.477170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:04.669031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:04.853986image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:05.042236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:05.215201image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:05.411496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:05.595459image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:05.771409image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:05.982823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:06.203659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:06.455679image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:06.637856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:06.800330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:06.969317image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:07.152118image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:07.344782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:07.504115image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:07.661060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:07.832921image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:08.003741image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:08.197553image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:08.410315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:08.609884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:08.841797image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:09.050616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:09.209993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:09.398905image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:09.602099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:09.758377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:09.918368image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:10.151897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:10.307243image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:10.444959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:10.642495image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:10.813049image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:10.959272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:11.149557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:11.327393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:11.470163image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:11.660412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:11.811210image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:11.980576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:12.157818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:12.294183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:12.418900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:12.580619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T21:40:12.745253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-18T21:40:18.960995image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-18T21:40:19.297786image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-18T21:40:19.627858image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-18T21:40:19.951119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-18T21:40:13.074460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-18T21:40:13.453734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.5000000.48611140.050.9705888.735294
11130473232.5956.09.01390.0171.018.90403527.2500000.04878035.0154.44444419.000000
22125836705.382.015.05028.0232.028.90250023.1875000.04569950.0335.20000015.466667
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000005.600000
4415100876.00333.03.080.03.0292.0000008.6000000.13636422.026.6666671.000000
55152914623.3025.014.02102.0102.045.32647123.2000000.05444129.0150.1428577.285714
66146885630.877.021.03621.0327.017.21978618.3000000.073569399.0172.42857115.571429
77178095411.9116.012.02057.061.088.71983635.7000000.03910641.0171.4166675.083333
881531160767.900.091.038194.02379.025.5434644.1444440.315508474.0419.71428626.142857
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714299.571429

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.0645.066.016.0643946.00.2857146.0645.00000066.0
2960563717232421.522.02.0203.036.011.70888912.00.1538460.0101.50000018.0
2961563817468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.5
2962564913596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000083.0
29635655148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.5
2964565912479473.2011.01.0382.030.015.7733334.00.33333334.0382.00000030.0
2965568014126706.137.03.0508.015.047.0753333.01.00000050.0169.3333335.0
29665686135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333145.0
2967569615060301.848.04.0262.0120.02.5153331.02.0000000.065.50000030.0
2968571512558269.967.01.0196.011.024.5418186.00.285714196.0196.00000011.0